Search found 9 matches
- Tue Aug 16, 2016 5:56 am
- Forum: State Space Models/DSGE
- Topic: UCM trend cycle decomposition
- Replies: 7
- Views: 11179
Re: UCM trend cycle decomposition
Thanks for your help Tom. So in this case, if I understand correctly, the issue is that the C and SV components need to be computed within the DLMStart function before the Kim filter is initialised as the remaining components with free parameters are already done so in the block with the start up co...
- Mon Aug 15, 2016 6:22 pm
- Forum: State Space Models/DSGE
- Topic: UCM trend cycle decomposition
- Replies: 7
- Views: 11179
Re: UCM trend cycle decomposition
Thanks Tom. When considering the Markov-switching trend I am taking a slightly different approach to the authors in the paper linked in my OP and amending the replication file for Kim's representation of Lam's generalised Hamilton model (kimnp111.rpf). I am continuing to use an auxiliary observable ...
- Sun Aug 14, 2016 10:08 am
- Forum: State Space Models/DSGE
- Topic: UCM trend cycle decomposition
- Replies: 7
- Views: 11179
Re: UCM trend cycle decomposition
Thanks Tom. Is the first DLM instruction which calls on the parmset=small necessary? I don't understand why the final values for all parameters are not jointly determined? I included it following the replication file for Clarke's bi-variate model in Kim and Nelson.
- Fri Aug 12, 2016 6:39 pm
- Forum: State Space Models/DSGE
- Topic: UCM trend cycle decomposition
- Replies: 7
- Views: 11179
UCM trend cycle decomposition
Hi Tom, I am trying to replicate some of the methods in the paper linked below, which uses an additional observable variable to better identify the cycle in an unobserved components model and also accounts for a markov switching drift term in the trend. I have started to deal with the first element ...
- Mon Jul 18, 2016 5:01 pm
- Forum: RATS for Teachers & Students
- Topic: Kim and Nelson, State-space Models with Regime Switching
- Replies: 15
- Views: 108504
Re: Kim and Nelson, State-space Models with Regime Switching
Hi Tom, thanks for posting these examples. I have a query on the setting of initial values in KIMNP111.RPF. Specifically the phi and x0 vectors compute phi=||1.2,-.3|| compute sigsq=%seesq * frml kimf = kf=KimFilter(t),log(kf) compute x0=||5.224,2.699|| Am I right to say the phi's are AR coefficient...
- Tue Jan 05, 2016 8:29 pm
- Forum: VARs (Vector Autoregression Models)
- Topic: BVAR Forecasting evaluation with @runtheil
- Replies: 1
- Views: 4614
BVAR Forecasting evaluation with @runtheil
Hi, I am trying to evaluate forecasts from a BVAR for multiple alternative values for the tightness parameters. A hypothetical example of what I'm doing derived from the canmodel.rpf is below: procedure runtheil option choice type 1 symmetric general option rect matrix option vector mvector option r...
- Wed Mar 11, 2015 5:29 pm
- Forum: VARs (Vector Autoregression Models)
- Topic: VECM with user specified ECT
- Replies: 1
- Views: 4209
VECM with user specified ECT
Hi, Is it possible to estimate a VECM and get the estimation output in first differences and impulse responses generated in levels while specifying the error correction term as a specific variable in the dataset and not the residuals from the long-run relationship that is estimated before setting up...
- Mon Feb 16, 2015 5:25 am
- Forum: Help With Programming
- Topic: Saving bootstrapped series
- Replies: 3
- Views: 6889
Re: Saving bootstrapped series
Thanks Tom. I am trying to avoid having to declare 500,000 series individually, which will then have the necessary series name to take the bootstrapped series. If I bring the declare command outside the loop it looks like below, and doesn't run (I tried shortening the series name to see if that was ...
- Sun Feb 15, 2015 5:36 am
- Forum: Help With Programming
- Topic: Saving bootstrapped series
- Replies: 3
- Views: 6889
Saving bootstrapped series
Hi Tom, I have a basic data management problem I can't fix, which builds on your reply to a previous post . I have a residual series for 500 individuals, with each series named res(m), m=1,2,...,500, and each res(m) series has different number of observations which are also in the dataset as series ...